Cloud-based low-cost smart care: Breathing test radar
Author(s)
Author(s)
Lau, C. F.
Date Issued
2024
Publisher
IEEE
Related Publication(s)
Proceedings of the 2024 IEEE International Conference on Big Data and Smart Computing (BigComp)
Start page
325
End page
328
Abstract
The current health care adopts smart care driven by data, utilizing multiple-sensor measurements. However, it is not straightforward how one may map the relationship of sensors using traditional machine learning methods alone. This paper introduces a method integrating a Graph Convolutional Network (GCN) with an odor-sensing array to extract the change in odor from respiratory information such as concentrations of Volatile Organic Compounds. This approach measures the differences in odor under different conditions of the subjects (e.g., 1. before and after exercise, 2. during COVID-19 sickness and after recovery) by learning the increasing concentration of gas mixtures from multiple sensors. GCN grasps the relationship between odor sensors' sensitivity and achieves an experimental accuracy rate of 81.6%. Since the graph structure is a scalable permutable domain, other odor-gain labels can potentially form a new feature learning based on this pivot feature learning.
SFU Affiliated Publication
Yes
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